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In this paper, we provide a new neural-network based perspective on multi-task learning (MTL) and multi-domain learning (MDL).
Musical genre classification of audio signals
Tzanetakis, G. and Cook, P · 2002
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Regularized multi–task learning
Evgeniou, T. and Pontil, M · 2004
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To transfer or not to transfer
Rosenstein, M. T., Marx, Z., Kaelbling, L. P., and Dietterich, T. G · 2005
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Frustratingly easy domain adaptation
Daumé III, H · 2007
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Multi-task learning for classification with dirichlet process priors
Xue, Y., Liao, X., Carin, L., and Krishnapuram, B · 2007
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Convex multi-task feature learning
Argyriou, A., Evgeniou, T., and Pontil, M · 2008
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Zero-data learning of new tasks
Larochelle, H., Erhan, D., and Bengio, Y · 2008
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Zero-shot domain adaptation: A multi-view approach
Blitzer, J., Foster, D. P., and Kakade, S. M · 2009
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Learning to detect unseen object classes by between-class attribute transfer
Lampert, C. H., Nickisch, H., and Harmeling, S · 2009
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Zero-shot learning with semantic output codes
Palatucci, M., Pomerleau, D., Hinton, G., and Mitchell, T · 2009
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Multi-domain learning by confidence-weighted parameter combination
Dredze, M., Kulesza, A., and Crammer, K · 2010
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Adapting visual category models to new domains
Saenko, K., Kulis, B., Fritz, M., and Darrell, T · 2010
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Learning with whom to share in multi-task feature learning
Kang, Z., Grauman, K., and Sha, F · 2011
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Unbiased look at dataset bias
Torralba, A. and Efros, A. A · 2011
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Effects of relevant contextual features in the performance of a restaurant recommender system
Vargas-Govea, B., González-Serna, G., and Ponce-Medellın, R · 2011
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Domain adaptations for computer vision applications
Beijbom, O · 2012
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Exploiting web images for event recognition in consumer videos: A multiple source domain adaptation approach
Duan, L., Xu, D., and Chang, S.-F · 2012
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Geodesic flow kernel for unsupervised domain adaptation
Gong, B., Shi, Y., Sha, F., and Grauman, K · 2012
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Multilinear multitask learning
Romera-paredes, B., Aung, H., Bianchi-berthouze, N., and Pontil, M · 2013
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Zero-shot learning through cross-modal transfer
Socher, R., Ganjoo, M., Manning, C. D., and Ng, A. Y · 2013
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Large-scale object classification using label relation graphs
Deng, J., Ding, N., Jia, Y., Frome, A., Murphy, K., Bengio, S., Li, Y., Neven, H., and Adam, H · 2014
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Latent low-rank transfer subspace learning for missing modality recognition
Ding, Z., Ming, S., and Fu, Y · 2014
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Transductive multi-view embedding for zero-shot recognition and annotation
Fu, Y., Hospedales, T., Xiang, T., Fu, Z., and Gong, S · 2014
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Continuous manifold based adaptation for evolving visual domains
Hoffman, J., Darrell, T., and Saenko, K · 2014
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Multi-domain learning: When do domains matter?
Joshi, M., Dredze, M., Cohen, W. W., and Rosé, C. P · 2012
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Learning task grouping and overlap in multi-task learning
Kumar, A. and Daumé III, H · 2012
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Flexible modeling of latent task structures in multitask learning
Passos, A., Rai, P., Wainer, J., and Daumé III, H · 2012
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Domain adaptive dictionary learning
Qiu, Q., Patel, V. M., Turaga, P., and Chellappa, R · 2012
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Devise: A deep visual-semantic embedding model
Frome, A., Corrado, G., Shlens, J., Bengio, S., Dean, J., Ranzato, M., and Mikolov, T · 2013
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The audio degradation toolbox and its application to robustness evaluation
Mauch, M. and Ewert, S · 2013
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Caffe: Convolutional architecture for fast feature embedding
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., and Darrell, T · 2014
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Multitask learning meets tensor factorization: task imputation via convex optimization
Kishan Wimalawarne, M. S. and Tomioka, R · 2014
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Low-rank tensor completion by riemannian optimization
Kressner, D., Steinlechner, M., and Vandereycken, B · 2014
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From virtual to reality: Fast adaptation of virtual object detectors to real domains
Sun, B. and Saenko, K · 2014
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Decaf: A deep convolutional activation feature for generic visual recognition
Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., and Darrell, T · 2015
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